---
title: "UIUC's DRPG Beats Human Researchers at Writing Academic Rebuttals"
subtitle: "A four-stage agentic pipeline breaks reviews into atomic concerns, retrieves evidence, plans strategies, and generates rebuttals that beat human averages using an 8B model."
url: "https://alphasignal.ai/news/uiuc-s-drpg-beats-human-researchers-at-writing-academic-rebuttals"
author: "AlphaSignal Newsroom"
author_url: "https://alphasignal.ai/editorial-team"
company: "AlphaSignal"
company_url: "https://alphasignal.ai/company/alphasignal"
published: "2026-04-13T00:00:00.000Z"
updated: "2026-08-24T03:00:12.809Z"
type: "Paper"
topics:
  - "Agents"
  - "Development"
subtopics:
  - "Agent Frameworks"
  - "Tool Use"
  - "Code Agents"
  - "Code Agents"
upvotes: 1176
source_url: "https://arxiv.org/abs/2601.18081"
comments: 0
truncated: true
---

# UIUC's DRPG Beats Human Researchers at Writing Academic Rebuttals

> A four-stage agentic pipeline breaks reviews into atomic concerns, retrieves evidence, plans strategies, and generates rebuttals that beat human averages using an 8B model.

Writing a good rebuttal to peer reviewers is one of the more painful rituals of academic life. It requires reading a long paper, picking apart each critique, and constructing arguments that are both accurate and persuasive, often under a tight deadline. A team at the University of Illinois Urbana-Champaign has now built an agentic system called [DRPG](https://arxiv.org/abs/2601.18081) that automates this workflow, and their experiments suggest it can outperform the average human rebuttal using only an 8B model.

## Why off-the-shelf LLMs choke on rebuttals

Major conferences such as NeurIPS and ICLR received over 25,000 submissions in 2025, and each of those papers typically triggers multiple reviews that authors have to answer point by point. Prior attempts to automate this have mostly leaned on zero-shot prompting or ad-hoc pipelines, which run into two structural problems.

The first is long context. Academic papers are lengthy and information-dense, and LLMs struggle to identify and extract the most relevant evidence when responding to specific reviewer concerns, a failure mode known as the "lost in the middle" effect. The second is persuasion. Because LLMs are not explicitly trained to persuade, they tend to produce responses that are overly generic, excessively conciliatory, or defensive, failing to convincingly address reviewers' key concerns.

## Four agents, one rebuttal

DRPG stands for Decompose, Retrieve, Plan, Generate. Each stage is handled by a dedicated component that hands structured output to the next one.

![DRPG pipeline diagram showing Decomposer, Retriever, Planner, and Executor stages](https://arxiv.org/html/2601.18081v2/main_fig.png) 
* **Decomposer:** An LLM that splits a review into atomic points, each capturing one weakness or question the reviewer raised.
* **Retriever:** Uses dense retrieval, where a text encoder embeds both review points and paper paragraphs and cosine similarity measures their relevance. Only the most relevant paragraphs are passed to the executor, increasing information density while cutting content length by around 75% in practice. Paragraphs are encoded with their section headers attached, so hierarchical context is preserved.

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## Sources

- [DRPG (Decompose, Retrieve, Plan, Generate): An Agentic Framework for Academic Rebuttal](https://arxiv.org/abs/2601.18081)
- [DRPG-RebuttalAgent GitHub Repository](https://github.com/ulab-uiuc/DRPG-RebuttalAgent)
- [BGE-M3 Text Encoder](https://huggingface.co/BAAI/bge-m3)
- [Re2: A consistency-ensured dataset for full-stage peer review and multi-turn rebuttal discussions](https://arxiv.org/abs/2505.07920)